A Data Resource for Sulfuric Acid Reactivity of Organic Chemicals

data

Data Descriptor A Data Resource for Sulfuric Acid Reactivity of Organic Chemicals

William Bains 1,* , Janusz Jurand Petkowski 1 and Sara Seager 1,2,3

1 Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts. Avenue., Cambridge, MA 02139, USA; [email protected] (J.J.P.); [email protected] (S.S.) 2 Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts. Avenue., Cambridge, MA 02139, USA 3 Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts. Avenue., Cambridge, MA 02139, USA * Correspondence: [email protected]

Abstract: We describe a dataset of the quantitative reactivity of organic chemicals with concentrated sulfuric acid. As well as being a key industrial chemical, sulfuric acid is of environmental and planetary importance. In the absence of measured reaction kinetics, the reaction rate of a chemical with sulfuric acid can be estimated from the reaction rate of structurally related chemicals. To allow an approximate prediction, we have collected 589 sets of kinetic data on the reaction of organic chemicals with sulfuric acid from 262 literature sources and used a functional group-based approach to build a model of how the functional groups would react in any sulfuric acid concentration from 60–100%, and between −20 ◦C and 100 ◦C. The data set provides the original reference data and kinetic measurements, parameters, intermediate computation steps, and a set of ﬁrst-order rate constants for the functional groups across the range of conditions −20 ◦C–100 ◦C and 60–100% sulfuric acid. The dataset will be useful for a range of studies in chemistry and atmospheric sciences where the reaction rate of a chemical with sulfuric acid is needed but has not been measured. Citation: Bains, W.; Petkowski, J.J.; Seager, S. A Data Resource for Dataset: doi: 10.5281/zenodo.4467868 Sulfuric Acid Reactivity of Organic Chemicals. Data 2021, 6, 24. https://doi.org/10.3390/ Dataset License: Dataset is made available under the Creative Commons license CC BY (SA). data6030024 Keywords: kinetics; reactivity; sulfuric acid; sulfonation; solvolysis Academic Editor: Igor I. Baskin

Received: 4 December 2020 Accepted: 22 February 2021 1. Summary Published: 25 February 2021 The chemistry of sulfuric acid is of practical importance to industry [1], as well as to atmospheric sciences and astronomy. The kinetics of reaction with sulfuric acid—whether Publisher’s Note: MDPI stays neutral a substance will react and how fast, under a wide range of conditions—is, therefore, of with regard to jurisdictional claims in great practical importance. The reactivity of a molecule in sulfuric acid may be calculated published maps and institutional afﬁl- exactly when the molecule’s pKa is known (and so its ionization state in the acid of iations. different concentrations is known), and the reaction mechanism is known (reviewed in [1–4]). However, these properties are rarely known unless the kinetics have already been experimentally measured for the compound of interest or a compound that is structurally closely related. Estimating the stability of new substances in sulfuric acid, therefore, must Copyright: © 2021 by the authors. rely on comparison with diverse chemicals containing functional groups similar to the test Licensee MDPI, Basel, Switzerland. molecule. The dataset presented in this paper provides a set of kinetic examples for such This article is an open access article reactivity estimation and modeling from literature data and an extrapolated set of kinetic distributed under the terms and parameters based on the experimentally derived examples. conditions of the Creative Commons Data on the reactivity of chemicals in sulfuric acid is scattered in diverse literature, Attribution (CC BY) license (https:// using diverse reporting methods and units. We compiled data on the reaction kinetics for creativecommons.org/licenses/by/ 589 reactions in concentrated sulfuric acid from 262 literature sources and converted them 4.0/).

Data 2021, 6, 24. https://doi.org/10.3390/data6030024 https://www.mdpi.com/journal/data Data 2021, 6, x FOR PEER REVIEW 2 of 10

Data on the reactivity of chemicals in sulfuric acid is scattered in diverse literature, Data 2021, 6, 24 2 of 10 using diverse reporting methods and units. We compiled data on the reaction kinetics for 589 reactions in concentrated sulfuric acid from 262 literature sources and converted them to a dataset in a unified system of units. Literature searches were both from citations toin abooks dataset and in apapers uniﬁed on system sulfuric of units.acid chemistry Literature and searches by keyword. were both In fromthe case citations of in phosphine,books anda model papers of reactivity on sulfuric was acid built chemistry from scattered and by keyword.literature hints In the to case fill ofin phosphine,a crucial parta model of chemical of reactivity space, was for builtreasons from de scatteredscribed below. literature The hintscompiled to ﬁll data in a were crucial then part of used chemicalto build space,a dataset for of reasons extrapolated described reacti below.on rates The compiledfor a set dataof 130 were functional then used groups to build a liabledataset to breakdown of extrapolated or solvolysis reaction in ratesconcen fortrated a set ofsulfuric 130 functional acid and groups a complementary liable to breakdown set of reactionor solvolysis parameters in concentrated for sulfonation sulfuric of phenyl acid and rings. a complementary set of reaction parameters Thefor sulfonationdata provided of phenyl by the rings.dataset can be used to give a pragmatically based indication of theThe stability data providedof an arbitrary by the chemical dataset can in sulfuric be used toacid. give Detailed a pragmatically mechanistic based studies indication (eitherof ab the initio stability quantum of an arbitrarycalculations chemical of reaction in sulfuric paths acid. or Detaileddetailed mechanisticexperimental studies kinetic (either studies)ab initiowouldquantum give much calculations more accurate of reaction predictions paths orbut detailed are extremely experimental time-consuming. kinetic studies) In theirwould absence, give we much believe more that accurate the dataset predictions presented but arein this extremely paper will time-consuming. be useful for any In their absence, we believe that the dataset presented in this paper will be useful for any researcher researcher looking to explore the reactivity of chemicals in sulfuric acid for applications looking to explore the reactivity of chemicals in sulfuric acid for applications such as, but such as, but not limited to, stratospheric chemistry or planetary atmospheric modeling. not limited to, stratospheric chemistry or planetary atmospheric modeling. The dataset The dataset will also have industrial applications, where sulfuric acid is a widely used will also have industrial applications, where sulfuric acid is a widely used reagent. In this reagent. In this regard, future work could extend this study to the reactivity of organic regard, future work could extend this study to the reactivity of organic compounds with compounds with nitric acid and with nitric and sulfuric acid mixtures, which are widely nitric acid and with nitric and sulfuric acid mixtures, which are widely used in nitration used in nitration reactions [5]. reactions [5].

2. Data2. DataDescription Description 2.1. Summary2.1. Summary of Data of Data The datasetThe dataset is a iscompilation a compilation of literature of literature data data on on sulfuric sulfuric acid acid reactivity reactivity of of mole- molecules, cules,converted converted to to unified unified data data units. units. The The data data fall fall into into three three parts: parts: • Original• Original literature literature data data(Excel (Excel Sheets Sheets “Data “Data”” and “References”); and “References”); • Functional• Functional group group definitions definitions (Excel (Excel Sheet Sheet “Groups “Groups List”) List”); ; • Derived• Derived kinetic kinetic constants constants (Excel (Excel Sheets Sheets “Concentration “Concentration Lookup”, Lookup”, “Group “Group defaults” defaults” and “Groupand “Group Rates Rates Matrix”). Matrix”). The derivedThe derived kinetic kinetic constants constants are extrapolated are extrapolated from fromthe literature the literature data datausing using the the functionalfunctional group group definitions, definitions, as described as described below below in Section in Section 3.5. 3.5. The structureThe structure of the of data the datais illustrated is illustrated in Figure in Figure 1. 1.

FigureFigure 1. Data 1. typesData and types their and relationships their relationships are presen areted presented in this paper. in this Top paper. row—the Top row—the data sets data sets presentedpresented here. Central here. Central row—the row—the relationship relationship between between the data thesets dataand their sets andinterconversion. their interconversion. BottomBottom row—types row—types of data of and data applications and applications that can that be can taken be takenfrom the from current the current data set data for set uses for as uses a as a literatureliterature resource, resource, a resource a resource for modeling for modeling or a lookup or a lookup table tablefor chemical for chemical stability. stability.

Data 2021, 6, 24 3 of 10

Chemical Structure Format (the SMILES Convention) Throughout, we have used the simpliﬁed molecular-input line-entry system (SMILES) notation to code molecular structure [6] and its extended SMARTS search notation [7]. This widely used notation allows coding of molecular topology as text.

2.2. Summary Statistics The original data comprise 589 reactions of 489 compounds identiﬁed from 262 literature references, most deriving from the period 1950–1980, but in total covering the period 1907–2019. The data were classiﬁed as to whether reactions were measured at a SINGLE acid concentration value or MULTIPLE acid concentrations, and whether they were measured at one temperature (“No_Q10”), multiple temperatures for one acid concentration (“SINGLE_Q10”), or multiple temperatures at multiple acid concentrations (“MULTIPLE_Q10”). The coverage of these combinations is summarized in Table1.

Table 1. Summary statistics.

No_Q10 SINGLE_Q10 MULTIPLE_Q10 SINGLE 182 14 0 MULTIPLE 326 38 31 Measurements of the kinetics of reactions in sulfuric acid, classiﬁed by the data available. SINGLE = rate measured at a single sulfuric acid concentration. MULTIPLE = rate measured at multiple concentrations. NO_Q10 = rate measured at one temperature, SINGLE_Q10 = rate measured at multiple temperatures at one sulfuric acid concentration, MULTIPLE_Q10 = rate measured at multiple temperatures and at multiple acid concentrations.

2.3. Data Schema The data are presented as an Excel file with multiple sheets; further files provide each sheet in a tab-separated variable (.TSV) format. All cells are free-form (i.e., not fixed length). All numbers are floating-point unless indicated otherwise. The data fields are summarized in Table2.

Table 2. Data ﬁelds in the ﬁle.

Field Name Field Description Sheet “Data”(in columns) NAME Compound name InChI IUPAC InChI code for the compound SMILES SMILES string for compound REACTION Type of chemical reaction undergone SINGLE_CONC For SINGLE type, the concentration of acid used SINGLE_CONC_UNITS For SINGLE type, units for concentration reported REF_TEMP Temperature at which reaction was reported SINGLE_RATE For SINGLE type, rate of reaction MUTLIPLE_GRADIENT For MULTIPLE type, gradient (m) in Equation (2) MULTIPLE_INTERCEPT For MULTIPLE type, intercept (c) in Equation (2) TIME_UNITS Units of time in which rates are reported (seconds, minutes, etc.) MULTIPLE_CONC_UNITS For MULTIPLE type, units of concentration for Equation (2) SINGLE_Q10 For SINGLE_Q10 type, value of Q10 SINGLE_Q10_CONC For SINGLE_Q10 type, concentration at which Q10 was calculated MULTIPLE_Q10_GRADIENT For MULTIPLE_Q10 type, gradient (n) in Equation (3) MULTIPLE_Q10_INTERCEPT For MULTIPLE Q10 type, intercept (d) in Equation (3) Data 2021, 6, 24 4 of 10

Table 2. Cont.

Field Name Field Description Sheet “Data”(in columns) REFERENCE Literature reference (see “Sheet reference”’ below) For MULTIPLE type, gradient (m) in Equation (2) normalized to % MULTIPLE_STANDARD_GRADIENT concentration units and seconds−1 rate units For MULTIPLE type, intercept (d) in Equation (2) normalized to % MULTIPLE_STANDARD_INTERCEPT concentration units and seconds−1 rate units SINGLE_STANDARD_CONC For SINGLE type, concentration converted to % acid SINGLE_STANDARD_RATE For SINGLE type, rate converted to seconds−1 GROUP Functional group number (see “Sheet, groups list” below) EXTRAPOLATED_EQUATION_GRADIENT Filled in gradient in Equation (2) as described in Section 3.5 below EXTRAPOLATED_EQUATION_INTERCEPT Filled in intercept in Equation (2) as described in Section 3.5 below EXTRAPOLATED_Q10_GRADIENT Filled in gradient in Equation (3) as described in Section 3.5 below EXTRAPOLATED_Q10_INTERCEPT Filled in intercept in Equation (3) as described in Section 3.5 below Sheet “Concentration lookup” Values of concentration of sulfuric acid in molar, and corresponding MOLAR and %_MOLAR values of % acid Values are Hammett acidity of sulfuric acid and corresponding Ho and %_Ho values of % acid Sheet “Groups list”(in columns) Number code used in GROUP column in data and group defaults NUMBER sheets NAME Internal name for this functional group SMARTS SMARTS string description of this functional group STRUCTURE Pictorial representation of structure Sheet “Group defaults” (in rows) GROUP Group number NAME Internal name for this functional group Default gradient for use in extrapolating SINGLE measurement GROUP_RATE_DEFAULT GRADIENT types for this functional group GROUP_Q10_DEFAULT Default Q10 value for this group Sheet “Group rates matrix” (in columns) GROUP Functional group number NAME Functional group name Columns 3 (C) thru 119 (DO): Row 1: Temperature Row 2: Acid concentration Log of reaction rate constant in seconds−1 of that functional group Rows 3 thru 132: (column 1) at the temperature and acid concentration speciﬁed at the head of the column Sheet “References” (in columns) References as cited in sheet “Data” Data 2021, 6, 24 5 of 10

3. Methods 3.1. Literature Search for Data The literature was searched for papers presenting kinetic data as follows: only papers that presented original rate measurements were considered; papers that presented summary kinetic equations or qualitative statements of rate were not included. Papers were identified (a) from citations in standard texts on sulfuric acid chemistry [1,2,4], (b) by search of Google Scholar using keywords “concentrated sulfuric” or “concentrated sulfuric” and “kinetics” or “rate of reaction”, and (c) search of all papers cited in or citing papers found by approaches (a) and (b). Roughly 1500 papers were scanned, of which 262 yielded usable data. All these papers reported the kinetic analysis of reaction of a compound containing a specific chemical group, here termed a functional group. Each molecule contained one and only one functional group. This is because the objective of all the papers was to study the mechanism of the reaction of that functional group. The molecule might contain other structures, but these were selected not to react under the conditions used. For example, the solvolysis of benzamides is studied in compounds that (by definition) contain phenyl rings [8,9], but the amide group is broken down many orders of magnitude faster than phenyl rings are sulfonated, and so in effect, the molecule can be considered as having only amide groups as a reacting moiety. The data for the reactivity of phosphine were compiled differently. Because of the interest in phosphine in the acid clouds of Venus [10], the reactivity of this molecule was of particular interest. However, no systematic kinetic data exists for the reaction of phosphine with sulfuric acid, despite the industrial importance of this reaction [11]. Single reaction points were, therefore, identified in five sources [12–16] and matched to Q10, m and c values from Equation (2) below. This is not ideal, as these are semi-anecdotal reports gathered up to a century ago under a wide range of conditions and illustrates the need for more systematic measurement of some literature values.

3.2. Data Checking and Validation The data within a paper were assessed for consistency by checking plots of rate vs. concentration with the paper’s own conclusions and for a smooth rate: concentration relationship. Outliers or sudden changes in curve gradient suggested an error, often in transcription, but sometimes in the original publication (usually typographical errors, such as a missing minus sign). The data were validated by internal reference within the dataset as follows: During compilation of kinetic parameters (see below), the scatter of predicted rates for a class of compounds was plotted for three sets of conditions. Typically ranges of rates were 1–2 log units. Outliers with a greater difference from the mean than 2 log units were checked against the original paper for transcription errors. No attempt was made to check a paper’s underlying scientiﬁc rationale. Not only was this impractical, but it was also not in the spirit of the database, which is to report what the literature says, not to critique it.

3.3. Rate Units Rates were assumed to be ﬁrst order with respect to the target chemical, i.e., repre- sented by a constant rate k with units of time−1. This is true of almost all reactions studied; for those where second-order kinetics applies at higher concentrations (e.g., [17,18]), ﬁrst- order kinetics are approximated at low concentrations. If rate constants or raw data were reported in units other than second−1, the rate was converted according to Equation (1) reported in time units other than seconds where:

rate(second) = rate(units)/(seconds/unit), (1)

where the rate(second) is the rate in units of seconds−1, rate(units) is the rate in some other time unit, and (seconds/unit) is the number of seconds in that time unit. Data 2021, 6, 24 6 of 10

Concentrations were converted to % acid units. Common concentration units were molar acid, molar water, and Hammett acidity Ho [1]. Speciﬁc tables for converting these units to % acid were constructed from [1,2,4,19,20], as the relationship between them is nonlinear. Lookup tables are provided in the sheet “Concentration Lookup” in the dataset. For some measurements, a rate of reaction at just a single concentration of sulfuric acid is reported (measurements we have designated SINGLE). For others, reaction rates at multiple concentrations are reported; such measurements are often used to determine the reaction mechanism. Reactions with rates measured at several sulfuric acid concentrations were designated MULTIPLE. For MULTIPLE entries, rates were matched to Equation (2)

Log(rate) = m ∗ (conc.) + c (2)

where m (the gradient of the linear plot of log(rate) vs. acid concentration) and c (the intercept of that plot) are constants, the rate is the rate in the reported units (see above), and conc. is the concentration of sulfuric acid. For MULTIPLE data entries, m and c were recorded in the dataset. A log-linear relationship was found to give a reasonable match to almost every data set for concentrations between 60% and 98%. Below 60%, some (but not all) reactions show substantially nonlinear kinetics with acid concentration due to competing reaction mechanisms. Above 98% species, such as SO3 and H2S2O7, become signiﬁcant players in reaction chemistry [21], Hammett acidity becomes highly nonlinear with concentration [1], and this simple formalism is less reliable.

3.4. Calculation of Q10 Values Reactions invariably proceed faster at higher temperatures, but the degree to which a reaction is accelerated by an increase in temperature depends on the activation energy of the reaction, which itself is dependent on the mechanism and is not readily inferred. The rate at which a reaction increases in rate is conveniently parameterized as a Q10 value—the multiple by which a reaction rate increases for a 10 ◦C increase in temperature. A wide range of temperature data was converted to a uniform Q10 parameter for the 83 reports that included data on rates at different temperatures. Q10 was calculated from the reports of the rate at a different temperature as follows: For a table of rate values and temperatures, starting with the lowest temperature, the gradient of a plot of (log(rate/rateo) vs. (T − To)/10 gave as gradient log(Q10), where the rate is the rate at temperature T, and rateo is the rate at temperature To. Some papers only allowed the calculation of Q10 for a single concentration of sulfuric acid (measurements designated SINGLE_Q10). This was reported in the database, together with the concentration at which it was measured. Others allowed calculations of Q10 for several acid concentrations (measurements, which were designated MULTIPLE_Q10). For these, the value of Q10 was plotted as a function of acid concentration, and the data set records the gradient and intercept of a straight line least-squares match through those data to form Equation (3): Q10 = n ∗ (conc.) + d (3)

Note that the majority of the entries had no Q10 data, as shown in Table1 (By contrast, all entries, by deﬁnition, had kinetic rate data).

3.5. Inference of Unmeasured Kinetic Parameters for Solvolysis Reactions The ideal would be to have MULTIPLE data and MULTIPLE_Q10 for all entries. As shown in Table1, this is not the case, and the data set needed to predict the reaction of a chemical at an arbitrary temperature and acid concentration is largely missing. For a complete picture of sulfuric acid reactivity, the missing rate values must be reconstructed from the single measured values and the typical rate at which the rate of reaction changes with acid concentration for that class of chemicals. This was achieved as follows: The pipeline is summarized in Figure2. Data 2021, 6, x FOR PEER REVIEW 7 of 10

complete picture of sulfuric acid reactivity, the missing rate values must be reconstructed Data 2021, 6, 24 from the single measured values and the typical rate at which the rate of reaction changes 7 of 10 with acid concentration for that class of chemicals. This was achieved as follows: The pipeline is summarized in Figure 2.

Figure 2. PipelineFigure for 2. Pipeline calculation for ofcalculation interpolated of inte kineticrpolated values kinetic for thevalues reactions for the in reactions the database. in the database. See Section See 3.5 text for details. YellowSection boxes 3.5 show text datafor details. types as Yellow listed boxes in Table show2. data types as listed in Table 2. Reactions that resulted in solvolysis or other breakdowns of the molecule (for example, Reactions that resulted in solvolysis or other breakdowns of the molecule (for ex- hydrolysis of esters, oxidation of thiols, dehydration of alcohols) were treated differently ample, hydrolysis of esters, oxidation of thiols, dehydration of alcohols) were treated from reactions involving sulfonation (adding an SO3 group to a molecule). Solvolysis differently from reactions involving sulfonation (adding an SO3 group to a molecule). reactions have kinetics that depends on the nature of the group being affected, and there is Solvolysis reactions have kinetics that depends on the nature of the group being affected, a wide range of possible groups. Sulfonations, by contrast, happens almost exclusively on and there is a wide range of possible groups. Sulfonations, by contrast, happens almost phenyl rings (for reasons discussed below) and hence have more uniform chemistry. exclusively on phenyl rings (for reasons discussed below) and hence have more uniform Solvolysis reactions are predicted on the basis of functional groups. A functional chemistry. group is deﬁned as a small group of atoms with characteristic chemical reactivity. It is Solvolysis reactions are predicted on the basis of functional groups. A functional expected that each functional group would have a characteristic sensitivity to attack by group is defined as a small group of atoms with characteristic chemical reactivity. It is sulfuric acid and that molecules containing a given functional group would have similar expected that mechanismseach functional of reaction group would with sulfuric have a acid. characteristic sensitivity to attack by sulfuric acid and thatEach molecules of the compounds containing whose a given reactions functional were group characterized would have was similar assigned to a func- mechanisms oftional reaction group with on sulfuric the basis acid. of the group that was being affected by sulfuric acid. As noted above, each compound contains only one functional group that is relevant for the conditions of the experiment, and hence each compound could be uniquely associated with one and only one functional group. Two sets of average properties for functional groups were then assigned: Data 2021, 6, 24 8 of 10

1. A mean gradient m from all the Equation (2). If no gradient was available for a functional group, i.e., there are no MULTIPLE values for that functional group, then the mean of the gradient m for the entire dataset is used instead. The result is designated GROUP_RATE_DEFAULT_GRADIENT; 2. A mean gradient for Q10 as a function of acid concentration for each functional group. If a functional group has no MULTIPLE_Q10 values, then the mean of the gradient of Q10 with acid concentration for the entire dataset is used instead. The result is designated GROUP_Q10_DEFAULT_GRADIENT. The following algorithm was then used to compile a set of reaction rates for each of the functional groups 1. Fill in a complete set of parameters for Equations (1) and (2) as follows: a. For MULTIPLE data: i. Rate gradients m and constants c for Equation (2) were taken from the experimental data b. For SINGLE data: i. Rate gradients m for Equation (2) were looked up in the table of GROUP_RATE_DEFAULT_GRADIENT values ii. Rate constants c for Equation (2) were calculated according to:

c = LOG(rate) − GROUP_RATE_DEFAULT_GRADIENT ∗ concentration, (4)

where rate = the single reaction rate measured by experiment and concentration is the concentration at which it is measured c. For MULTIPLE_Q10 data i. Gradient n and constant d in Equation (3) relating Q10 to acid concentration were taken from experimental data d. For SINGLE_Q10 data i. Gradient n in Equation (3) relating Q10 to acid concentration were looked up in the table GROUP_Q10_DEFAULT_GRADIENT ii. The constant d in Equation (3) was calculated according to Equation (5):

d = Measured_Q10 − GROUP_Q10_DEFAULT_GRADIENT ∗ concentration, (5) where Measured_Q10 is the single measured value for Q10, and concentration is the concentration of acid at which it was measured e. For No_Q10 values (i.e., measurements where no data on the dependence on rate with temperature was published) i. Gradient n in Equation (3) relating Q10 to acid concentration were looked up in the table of GROUP_Q10_DEFAULT_GRADIENT ii. The constant d in Equation (3) was assumed to be the global average value of d = 3.41612 2. Calculate rates for all molecules based on the interpolated constant data. With the “ﬁlled in” table of values for m, c, n and d, we calculate the predicted rate constant k for each reaction in the original dataset according to Equation (6):

( − ) ( + ) T To k = 10 m.acid c ·(n.acid + d) 10 (6)

where To is the temperature at which that reaction was originally measured, T is the temperature we wish to predict, acid is the concentration of acid in which we wish to predict reaction rate, in units of %, m, c, n and d are the parameters derived above, and k is the rate constant in seconds−1. The selection of T and acid is arbitrary, but to build a systematic table in which values can easily be looked up, a matrix of temperatures from −20 ◦C to 100 ◦C and 60% acid to 100% acid was used. Data 2021, 6, 24 9 of 10

3. Average rates for all reactions in each functional group, to make an average rate for that functional group The ﬁnal output of the algorithm is a table of reaction rates for each of the functional groups for the whole matrix of conditions modeled in Equation (6). This matrix is provided in the sheet “Group Rates Matrix” as the Log10(rate constant)

3.6. Inference of Unmeasured Kinetic Parameters for Sulfonation Reactions Unlike solvolysis reactions, sulfonation reactions are effectively the chemistry of phenyl rings. Sulfonation only occurs in concentrated sulfuric acid and at elevated temperatures. A range of reagents can sulfonate other groups and can sulfonate phenyl at lower temperatures [2], but this chemistry is not relevant to the chemistry of sulfuric acid itself. Thus, groups that are less stable to solvolysis than phenyl rings will be attacked and degraded before they are sulfonated (e.g., furan rings), and nitrogen-containing aromatics are resistant to any sulfuric acid chemistry and can only be sulfonated by more potent sulfonating agents, such as “oleum” (solutions of SO3 in pure sulfuric acid) [22,23]. For phenyl rings, sulfonation is accelerated or retarded depending on the electronic effects of substituents on the rings. Sulfonation rates for a phenyl ring with arbitrary substituents were calculated as follows: A base rate for sulfonation of the phenyl ring was obtained directly from experimental data, as described above. Sulfonation for a substituted phenyl ring was estimated as z rate = Rp· ∏ Si (7) i=1,n

where Rp is the rate of sulfonation of benzene, Si is the multiplicative factor for substituent i, and z is the number of i substituents on the ring. Multiplicative factors for the addition of substituents to the ring were then estimated using a simulated annealing algorithm [9] to minimize the difference between predicted values and experimental values for the range of substituted phenyls present in the original experimental dataset under the same matrix of conditions described in Section 3.5, algorithm step 2 above. The output of the algorithm is a table of “base rates” of reaction for unmodiﬁed benzene at the same selected temperatures and acid concentrations as for the solvolysis z reactions in Section 3.5 above, and a set of Si parameters in the sheet “group defaults”.

4. User Notes The dataset is available for downloading from zenodo.org as an Excel 2010 spreadsheet and as separate TSV files representing each of the Excel sheets, which, together with a “readme” file, are presented as a *.zip archive. We suggest that there are three ways the data could be used. • Lookup of reaction rates for specific functional groups, using tables in the sheets “group defaults” (for sulfonation) and “group rates matrix” (for solvolysis) for a specific set of conditions; • Lookup interpolated kinetic data from the sheet “data” to build a customized database of rates, either for a customized set of conditions or using a different averaging schema; • Use the primary data from the datasheet to build a new algorithm for “filling in” missing data and/or trace original data from the references listed.

Author Contributions: Project conceptualization; S.S., W.B., J.J.P.; methodology W.B.; software, J.J.P.; data collection and processing W.B.; writing—original draft preparation, W.B., writing—review and editing, J.J.P., S.S., W.B.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Change Happens Foundation, and by Breakthrough Initiatives. Data Availability Statement: Data is downloadable from Zenodo.org using doi:10.5281/zenodo.4467868, under the Creative Commons license CC BY (SA). Data 2021, 6, 24 10 of 10

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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